Model Predictive Mean Field Games for Controlling Multi-Agent Systems
Abstract
When controlling multi-agent systems, the trade-off between performance and scalability is a major challenge. Here, we address this difficulty by using mean field games (MFGs), which is a framework that deduces the macroscopic dynamics describing the density profile of agents from their microscopic dynamics. To effectively use the MFG, we propose a model predictive MFG (MP-MFG), which estimates the agent population density profile with using kernel density estimation and manages the input generation with model predictive control. The proposed MP-MFG generates control inputs by monitoring the agent population at each time step, and thus achieves higher robustness than the conventional MFG. Numerical results show that the MP-MFG outperforms the MFG when the agent model has modeling errors or the number of agents in the system is small.
Keywords
Cite
@article{arxiv.2004.07994,
title = {Model Predictive Mean Field Games for Controlling Multi-Agent Systems},
author = {Daisuke Inoue and Yuji Ito and Takahito Kashiwabara and Norikazu Saito and Hiroaki Yoshida},
journal= {arXiv preprint arXiv:2004.07994},
year = {2021}
}
Comments
This paper has been accepted for 2021 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC2021)